Factors impacting on EMSR Composite Index Score: Multiple

The purpose of this section is to examine whether there is a linear relation between the EMSR Composite Index score and: size of company; location of operations; percentage of operations in developing countries; number of operating subsidiaries in developing countries. The explanatory variables in this analysis are quantitative and ordinal or continuous (the measurement of all variables are in Table 5).

The multivariate linear regression equation is:

Yi = a0 + b1 X1i + b2 X2i + ... + ei for i = 1, 2, ..., n

Where :

Yi represents the dependent variable

X1i, X2i, ... are the values of a set of independent variables

n is the number of gathered observations;

ei is the error term;

bi are the regression coefficients whose numerical values are to be

estimated by the regression analysis (Gujarati, 2011).

The following assumptions are necessary for the implementation of the MLR: 1. The dependent variable must be continuous and normally distributed. 2. The independent variables can be either continuous or binary.

3. The existence of a linear correlation between dependent and the independent variables

4. The non-existence of multicollinearity among the independent variables 5. Residuals need to be normally distributed, to be independent, and to

89 In our analysis, the EMSR Index is a continuous variable, which take a value in the scale between 0 and 12, and therefore is suitable to be used as a dependent variable in the regression model. Table 9 summarises its descriptive statistics.

Table 9 - Descriptive Statistics Table

Statistic _Error Std. Mean 5.1875 0.41825 Median 5 Variance 9.796 Std. Deviation 3.12986 Minimum 0 Maximum 12 Range 12 Interquartile Range 4 Skewness 0.237 0.319 Kurtosis -0.275 0.628

The average EMSR Index is 5.19 with a median of 5.00, the minimum value of the Index is 0, and the maximum is 12. The fact that the EMSR Index presents similar values for its mean and median is the first indication that the dependent variable follows a normal distribution, a factor that constitutes the first prerequisite for the implementation of the model. This is confirmed by the histogram in Figure 1.

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Figure 1 - Histogram of EMSR Index Residuals

The assumption for the normal distribution of the dependent variable is tested applying the Kolmogorov-Smirnov test.

Ho: the dependent variable is normally distributed

H1: the dependent variable is not normally distributed

Results: Kolmogorov-Smirnov test statistic = 0.085, p-value = 0.20 > 0.05

Therefore, we accept the null hypothesis (Ho) that the EMSR Index variable is

normally distributed.

The explanatory variables (See Table 11) are four continuous variables of: size of company; location of operations, percentage of operations in developing countries; number of operating subsidiaries in developing countries.

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Table 10 - Multiple Linear Regression Data

Total Overall Total Subsidiaries Market Cap Total Developing Percent Developing Total Overall Pearson Correlation 1 .469** 0.257 .524** .346** Sig. (2- tailed) 0 0.055 0 0.009 Total Subsidiaries Pearson Correlation .469** 1 .656** .973** 0.04 Sig. (2- tailed) 0 0 0 0.768 Market Cap Pearson Correlation 0.257 .656** 1 .688** 0.039 Sig. (2- tailed) 0.055 0 0 0.778 Total Developing Pearson Correlation .524** .973** .688** 1 0.171 Sig. (2- tailed) 0 0 0 0.207 Percent Developing Pearson Correlation .346** 0.04 0.039 0.171 1 Sig. (2- tailed) 0.009 0.768 0.778 0.207

**. Correlation is significant at the 0.01 level (2-tailed).

Table 10 shows that there is a significant Market Cap relationship between each of the explanatory variables and the dependent variable (Total Overall Score and: Total number of countries with subsidiaries, r = 0.469, p=0.000 < 0.01; Market Cap, r = 0.257, p=0.055 <0.10; No. of Developing countries with subsidiaries, r = 0.524, p=0.00 < 0.01; Proportion in Developing countries, r=0.346, p=0.009 < 0.01).

A very high significant correlation was detected between two explanatory variables (‘Total number of countries with subsidiaries’ and ‘Number of Developing Countries with subsidiaries’), and as a result the variable ‘Total number of countries with subsidiaries’ was removed for the model.

92 The new model that will be estimated is:

∗ ∗

∗

The null hypothesis is that the b-coefficients are zero, meaning no significant relationship between the dependent variable and any of the explanatory variables.

The alternative hypothesis is that the b-coefficients are not zero (either positive or negative), meaning there is a significant relationship between the dependent variable and the explanatory variables. A significance level of 5% is adopted to accept or reject the null hypothesis.

The case for multicollinearity is tested after the implementation of MLR. Within this test, SPSS calculates two different indexes: the index of tolerance and the Variance Inflation Factor (VIF). In order to avoid the phenomenon of multicollinearity in our model the tolerance index should be greater than o.1 while VIF must be less than 10 for each of the independent variables. The assumptions of the residuals to be homoskedastic and lack of autocorrelation are tested after the estimation of the model.

The test for homoskedasticity and lack of autocorrelation of the residuals are conducted after the estimation of the model. This assumption is tested graphically in SPSS. It requires that at each level of the predictor variables, the variance of the residual terms should be constant. Where heteroskedasticity is present, this may suggest that the variances of the variables included in the analysis vary with the effects being modelled. In this scenario, while the variables may still be considered unbiased, they may be inefficient due to an underestimation of the actual variance and co-variance (Gujarati, 2009).

Residuals are also tested for lack of autocorrelation, a characteristic of the data in which for any two observations the residual terms should be uncorrelated (Greene, 2011). This assumption is tested with the Durbin-Watson test, which tests for serial correlation between errors. The size of the Durbin-Watson

93 statistic depends upon the number of predictors in the model and the number of observations. The test statistic can vary between 0 and 4 with a value of 2 meaning that the residuals are uncorrelated (Field, 2005: 170).